While human beings have an intuitive ability to understand language, getting machines to understand language remains a complicated problem. The field of Artificial Intelligence has been working on understanding language for decades. But to date systems either have a very limited ability to understand language in general, or a significant ability to understand language in a very specialized subset of language. For example, voice response systems tend to have fairly limited vocabularies: outside the words the systems are designed to understand, they are lost.
Textual analysis has not fared better than understanding spoken language. With textual analysis, the entirety of the text is present, and (usually) in a form that leaves the system with no uncertainty about the specific letters being used (ignoring the problems of character recognition from scanned text). But systems still have difficulty understanding what the text represents. For example, the sentence “The old man the boats” can confuse systems that think the word “old” is an adjective and the word “man” is a noun, where in fact the word “old” is a noun and the word “man” is a verb.
Aside from the problem of discerning the intended meaning of various words—words that can be used in different parts of a sentence, or words that are homophones, for example—systems have difficulty discerning the underlying context of text. People when they write, no less than when they speak, convey an attitude about a subject. But systems have difficulty understanding what the writer's attitude is.
A need remains for a way to address these and other problems associated with the prior art.